CN112801188A - NILMD method based on affine propagation clustering algorithm and selective Bayesian classification - Google Patents
NILMD method based on affine propagation clustering algorithm and selective Bayesian classification Download PDFInfo
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Abstract
The invention discloses an NILMD method based on affine propagation clustering algorithm and selective Bayesian classification, which comprises the following steps: calculating the running state of a sample target based on an AP algorithm; performing probability fitting on the running state of the sample target based on selective Bayesian classification to obtain a running state interval of the sample target so as to realize discretization of the running state of the sample target; and based on a two-dimensional Gaussian distribution and maximum likelihood estimation method, the sample target is identified and decomposed by combining the running state interval of the sample target, and the non-invasive load identification and decomposition of the sample target are completed. The invention solves the problem of poor recognition and decomposition precision of the traditional load decomposition method, and carries out sub-invasive load recognition and decomposition on the sample target by combining the AP algorithm and the selective Bayesian classification method, thereby realizing high-precision recognition and decomposition and improving the working efficiency.
Description
Technical Field
The invention relates to the technical field of electric power, in particular to an NILMD (network independent distance decomposition) method based on an affine propagation clustering algorithm and selective Bayesian classification.
Background
With the improvement of the power utilization level of industries and residents, the power utilization characteristics of the urban power grid demand side show a diversified development trend, and the influence of load differentiation characteristics including power utilization time intervals, load sizes, evaluation indexes, curve forms and the like on power grid scheduling and maintenance is increased year by year.
In the above sea power grid as an example, the construction background of 'digital pump power' and the strategic guidance of 'one, two, six and six' are considered, and in order to promote the urban energy Internet state development and the company digital transformation, the intellectualization and refinement level of power grid dispatching is comprehensively improved, and the non-invasive identification and decomposition for the resident load is the current important requirement.
In the eighties of the last century, George w.hart proposed a non-invasive load monitoring and decomposition (NILMD) method for more conveniently obtaining power consumption data of various electrical appliances of users. Firstly, to obtain the electrical data of a user bus, relevant data monitoring equipment is installed at an inlet of electrical equipment of a user, then the data is analyzed, and the running state of the electrical appliance is analyzed by means of a load identification algorithm.
However, with the development of intelligent measurement technology and the rapid increase of power consumption information on the load side, the existing load decomposition method cannot meet the actual requirement, and a new method is needed to realize the NILMD.
Disclosure of Invention
The invention aims to provide an NILMD method based on an affine propagation clustering algorithm and selective Bayesian classification. The method aims to solve the problem that the traditional load decomposition method is poor in identification and decomposition precision, and the sample target is subjected to sub-invasive load identification and decomposition by combining an AP algorithm and a selective Bayesian classification method, so that high-precision identification and decomposition are realized, and the working efficiency is improved.
In order to achieve the above purpose, the present invention provides an NILMD method based on an affine propagation clustering algorithm and selective bayesian classification, comprising the following steps:
step 1: calculating the running state of a sample target based on an affine propagation clustering (AP) algorithm;
step 2: performing probability fitting on the running state of the sample target based on selective Bayesian classification to obtain a running state interval of the sample target so as to realize discretization of the running state of the sample target;
and step 3: and based on a two-dimensional Gaussian distribution and maximum likelihood estimation method, the sample target is identified and decomposed by combining the running state interval of the sample target, and the non-invasive load identification and decomposition of the sample target are completed.
Most preferably, the AP algorithm comprises the steps of:
step 1.1: constructing a characteristic sample set of the sample target based on the low-frequency characteristics of the sample target;
step 1.2: updating the responsibility and the credibility of the characteristic sample set;
step 1.3: stabilizing oscillation of the characteristic sample set in the iterative process through a damping factor;
step 1.4: and repeating iteration on the characteristic sample set to obtain the running state of the characteristic sample set.
Most preferably, constructing a sample set of characteristics of the sample object comprises the steps of:
step 1.1.1: searching a clustering center of each data point of a sample target in the network and a membership between the data point and the data center through a message transfer mechanism;
step 1.1.2: and dividing the sample targets according to the membership between the data center and the vertex to form a characteristic sample set.
Most preferably, before performing the probability fitting, the method further comprises: and performing probability calculation on the feature sample set and the category variable to obtain the posterior probability of the feature sample set.
Most preferably, the probability fitting by the selective bayesian classification is performed by a bayesian classifier (SBC).
Most preferably, the identification of the sample objects comprises the steps of:
step 3.1: based on two-dimensional Gaussian distribution, performing stochastic description on the operation state interval to construct a power fluctuation model;
step 3.2: and identifying and decomposing the sample target based on a maximum likelihood estimation method and in combination with a power fluctuation model to finish non-invasive load identification and decomposition.
By using the method, the problem of poor recognition and decomposition precision of the traditional load decomposition method is solved, and the sample target is subjected to sub-invasive load recognition and decomposition by combining an AP algorithm and a selective Bayesian classification method, so that high-precision recognition and decomposition are realized, and the working efficiency is improved.
Compared with the prior art, the invention has the following beneficial effects:
the NILMD method provided by the invention solves the problem of poor identification and decomposition precision of the traditional load decomposition method, and carries out sub-invasive load identification and decomposition on the sample target by combining the AP algorithm and the selective Bayesian classification method, thereby realizing high-precision identification and decomposition and improving the working efficiency.
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FIG. 1 is a schematic flow diagram of the NILMD method provided by the present invention.
Detailed Description
The invention will be further described by the following specific examples in conjunction with the drawings, which are provided for illustration only and are not intended to limit the scope of the invention.
The invention provides an NILMD method based on affine propagation clustering algorithm and selective Bayesian classification, as shown in figure 1, comprising the following steps:
step 1: calculating the running state of a sample target based on an affine propagation clustering (AP) algorithm and combined with continuous variable state electrical appliances with concentrated load characteristics;
wherein the affine propagation clustering (AP) algorithm comprises the following steps:
step 1.1: based on the low-frequency characteristics of the sample target with active power and reactive power as main components, constructing a characteristic sample set X of the sample target, wherein X is { X }1,x2,…,xn}；
In this embodiment, a sample set X ═ X of characteristics of the sample object is defined1,x2,,xnThe sequence is the total power sequence P of the sample target with the time period T(j)Extracting power sequences P of M sample targets; wherein the content of the first and second substances,total power sequence P of sample targets of time period T(j)Satisfies the following conditions:
the power sequences P of the M sample targets satisfy:
P＝[P1,P2,…,PT]；
in the present embodiment, the time period T is 10 days; the sample target is a household appliance; sample targets included three household appliances, kitchen refrigerator, electric fan and immersion heater.
Wherein, constructing a characteristic sample set X ═ { X of the sample object1,x2,…,xnThe method specifically comprises the following steps:
step 1.1.1: searching a clustering center of each data point of a sample target in the network and a membership between the data point and the data center through a message transfer mechanism;
step 1.1.2: dividing sample targets according to membership between the data center and the vertexes to form a plurality of characteristic sample sets X ═ X with specific significance1,x2,…,xn}；
Wherein the dividing the sample target comprises: let similarity matrix Kn,nThe median h of (a) is a self-reference and is assigned to Kn,nDiagonal elements of (a).
Step 1.2: for any two samples X in the characteristic sample set XiAnd xjUpdating the responsibility r (i, j) and the credibility a (i, j) respectively; wherein, the update responsibility r (i, j) specifically satisfies:
where s (i, j) is any two samples X in the feature sample set XiAnd xjNegative value of euclidean distance of; a (i, j') is the confidence level after updating; j' is the running state of the characteristic sample set;
the update reliability a (i, j) specifically satisfies:
step 1.3: stabilizing oscillation of the characteristic sample set X in the iterative process by a damping factor lambda to accelerate convergence; the mth iteration of the feature sample set X satisfies:
rm(i,j)＝λrm-1(i,j)+(1-λ)rm(i,j)
am(i,j)＝λam-1(i,j)+(1-λ)am(i,j)
step 1.4: repeating iteration on the characteristic sample set X to obtain the running state j' of the characteristic sample set X, finishing iteration and realizing state discretization of a sample target; wherein, the running state j' of the characteristic sample set X satisfies the following conditions:
all data points in the AP algorithm are potential clustering centers, the granularity of clustering results can be controlled by adjusting the self-reference degree, the larger the self-reference degree value is, the more the number of clustering results is, and the smaller the self-reference degree value is, the smaller the number of clustering results is.
Step 2: based on selective Bayesian classification, performing probability fitting on the operation state j' of the sample target to obtain an operation state interval to which the operation power of the sample target belongs so as to realize discretization of the operation state of the sample target;
wherein, based on the selective Bayesian classification, the method also comprises the following steps before probability fitting: for the feature sample set X ═ X1,x2,…,xnAnd l class variables CiPerforming probability calculation to obtain posterior probability P (C) of the characteristic sample set Xi| x), and satisfies:
wherein P (X) is X ═ X1,x2,…,xn-probability of co-occurrence; p (C)i) Is a prior probability of class i, which is equal to the ratio of the number of samples for which i belongs to the class to the total number of samples, and satisfies:
where p (x) is a constant, the classification result is not affected and can be ignored, and therefore, the above formula can be expressed as:
for feature xjThe conditional probability P (x) is calculated by the following formulaj|Ci)：
In the formula niThe number of samples belonging to the category i in the sample set; k (x)j,xj,i,m,θi) As a nuclear parameter of thetaiA gaussian kernel function of; x is the number ofj,i,mIs xjThe value of the mth iteration in category i.
In the embodiment, the probability fitting of the selective Bayesian classification is realized by a Bayesian classifier (SBC); a Selective Bayesian Classifier (SBC) calculates the conditional probability of a feature by a gaussian kernel based kernel density estimation method, i.e. fitting a probability density function using a superposition of multiple gaussian distributions.
And step 3: and based on a two-dimensional Gaussian distribution and maximum likelihood estimation method, the sample target is identified and decomposed by combining the running state interval of the sample target, and the non-invasive load identification and decomposition of the sample target are completed.
Wherein, identifying the sample target respectively comprises the following steps:
step 3.1: the steady state power of the discretized sample target continuously changes in an operation state interval with the state power as the center, and the continuous change follows Gaussian distribution; the randomness of active power and reactive power under different running states is described based on two-dimensional Gaussian distribution, and a power fluctuation model f (X) is constructedT) (ii) a And the power fluctuation model f (X)T) Satisfies the following conditions:
wherein cov (X) is a covariance matrix; x is an observation vector; mu is an observation vector mean value;
step 3.2: based on maximum likelihood estimation method, combining power fluctuation model f (X)T) And identifying and decomposing the sample target to complete non-invasive load identification and decomposition.
In the present embodiment, the power fluctuation function of the qth household appliance among the Q household appliances at the time t is
Based on a maximum likelihood estimation method, the power decomposition satisfies the following steps:
wherein, P(q)The active power of the qth household appliance at the moment t; q(q)The reactive power of the qth household appliance at the moment t; p(q)allThe active power of the total load at the moment; q(q)allIs thatReactive power of the total load at that moment.
The working principle of the invention is as follows:
calculating the running state of a sample target based on an AP algorithm; performing probability fitting on the running state of the sample target based on selective Bayesian classification to obtain a running state interval of the sample target so as to realize discretization of the running state of the sample target; and based on a two-dimensional Gaussian distribution and maximum likelihood estimation method, the sample target is identified and decomposed by combining the running state interval of the sample target, and the non-invasive load identification and decomposition of the sample target are completed.
In conclusion, the NILMD method based on the affine propagation clustering algorithm and the selective Bayesian classification solves the problem of poor recognition and decomposition precision of the traditional load decomposition method, and the sample target is subjected to sub-invasive load recognition and decomposition by combining the AP algorithm and the selective Bayesian classification method, so that high-precision recognition and decomposition are realized, and the working efficiency is improved.
While the present invention has been described in detail with reference to the preferred embodiments, it should be understood that the above description should not be taken as limiting the invention. Various modifications and alterations to this invention will become apparent to those skilled in the art upon reading the foregoing description. Accordingly, the scope of the invention should be determined from the following claims.
Claims (6)
1. An NILMD method based on affine propagation clustering algorithm and selective Bayesian classification is characterized by comprising the following steps:
step 1: calculating the running state of the sample target based on an affine propagation clustering algorithm;
step 2: performing probability fitting on the operation state based on selective Bayesian classification to obtain an operation state interval of a sample target;
and step 3: and based on a two-dimensional Gaussian distribution and a maximum likelihood estimation method, the sample target is identified and decomposed by combining the operation state interval, and the non-invasive load identification and decomposition of the sample target are completed.
2. The NILMD method based on affine propagation clustering algorithm and selective bayesian classification according to claim 1, wherein said affine propagation clustering algorithm comprises the following steps:
step 1.1: constructing a characteristic sample set of the sample target based on the low-frequency characteristics of the sample target;
step 1.2: updating the responsibility and the credibility of the characteristic sample set;
step 1.3: stabilizing oscillation of the feature sample set in the iterative process through a damping factor;
step 1.4: and repeating iteration on the characteristic sample set to obtain the running state of the sample target.
3. The NILMD method based on affine propagated clustering algorithm and selective bayesian classification as claimed in claim 2, wherein constructing the feature sample set comprises the steps of:
step 1.1.1: searching a clustering center of each data point of a sample target in the network and a membership between the data point and the data center through a message transfer mechanism;
step 1.1.2: and dividing the sample targets according to the membership to form the characteristic sample set.
4. The NILMD method based on affine propagated clustering algorithm and selective bayesian classification as claimed in claim 1, wherein prior to performing said probability fitting further comprises: and performing probability calculation on the feature sample set and the category variable to obtain the posterior probability of the feature sample set.
5. The NILMD method based on affine propagated clustering algorithm and selective Bayesian classification as claimed in claim 1, wherein said selective Bayesian classification for said probability fitting is implemented by a Bayesian classifier.
6. The NILMD method based on affine propagated clustering algorithm and selective bayesian classification according to claim 1, wherein said identification of sample objects comprises the following steps, respectively:
step 3.1: based on two-dimensional Gaussian distribution, performing stochastic description on the operation state interval to construct a power fluctuation model;
step 3.2: and identifying and decomposing the sample target by combining the power fluctuation model based on a maximum likelihood estimation method to finish non-invasive load identification and decomposition.
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